Open Access
Issue |
E3S Web of Conf.
Volume 393, 2023
2023 5th International Conference on Environmental Prevention and Pollution Control Technologies (EPPCT 2023)
|
|
---|---|---|
Article Number | 03003 | |
Number of page(s) | 4 | |
Section | Pollution Control and Waste Recycling | |
DOI | https://doi.org/10.1051/e3sconf/202339303003 | |
Published online | 02 June 2023 |
- Serdarevic, A., & Dzubur, A. (2016). Wastewater process modeling. Coupled Systems Mechanics, 5: 21–39. [CrossRef] [Google Scholar]
- Sahith, J. K., & Lal, B. (2022). Artificial Intelligence in Water Treatment Process Optimization. Gas Hydrate in Water Treatment, 133: 139–153. [CrossRef] [Google Scholar]
- Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18: 100330. [CrossRef] [Google Scholar]
- Artificial Neural Network Tutorial. https://www.javatpoint.com/artificial-neural-network [Google Scholar]
- Pregowska, A., & Osial, M. (2021) What Is An Artificial Neural Network And Why Do We Need It? Frontiers for Young Minds. https://kids.frontiersin.org/articles/10.3389/frym.202 1.560631 [Google Scholar]
- Lek, S., & Park, Y. (2008). Artificial Neural Networks. Encyclopedia of Ecology, 237–245. [Google Scholar]
- Hamed, M. M., Khalafallah, M. G., & Hassanien, E. A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling &Amp; Software, 19(10): 919–928. [CrossRef] [Google Scholar]
- Hassen, E. B., & Asmare, A. M. (2019). Modeling and monitoring of treated wastewater based on water quality assurance parameters. Chemistry International, 5: 87-96 [Google Scholar]
- CAPODAGLIO, A. (1991). Sludge bulking analysis and forecasting: Application of system identification and artificial neural computing technologies. Water Research, 25: 1217–1224. [CrossRef] [Google Scholar]
- Jana, D. K., Bhunia, P., Das Adhikary, S., & Bej, B. (2022). Optimization of Effluents Using Artificial Neural Network and Support Vector Regression in Detergent Industrial Wastewater Treatment. Cleaner Chemical Engineering, 3: 100039. [CrossRef] [Google Scholar]
- Wang, J. H., Zhao, X. L., Guo, Z. W., Yan, P., Gao, X., Shen, Y., & Chen, Y. P. (2022). A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants. Environmental Research, 211: 113054. [CrossRef] [PubMed] [Google Scholar]
- Alsulaili, A., & Refaie, A. (2020). Artificial neural network modeling approach for the prediction of five-day biological oxygen demand and wastewater treatment plant performance. Water Supply, 21: 1861–1877. [Google Scholar]
- Alam, G., Ihsanullah, I., Naushad, M., & Sillanpää, M. (2022b). Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects. Chemical Engineering Journal, 427: 130011. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.